Efficient inventory management is essential for retail companies, significantly impacting customer satisfaction and cost efficiency. However, managing related processes is highly complex due to the dynamic interaction of multiple object types along the supply chain (e.g. suppliers, customers, materials) and the reliance on diverse and fragmented information systems. Consequently, retail companies face challenges with inefficient inventory structures, including understock (loss of sales) and overstock (high capital commitment). This paper proposes an object-centric process mining approach to analyze process-related causes of inefficient inventory management. A novel Object-Centric Data Model (OCDM) is introduced, capturing key entities and interactions across inventory management-related processes. Unlike traditional models, the OCDM is enriched with established inventory metrics, enabling multi-perspective analyses that link process interactions to metrics such as understock, healthy stock, and overstock. This approach uncovers inventory management inefficiencies and their process-related root causes, delivering actionable insights. A case study at a leading European pet retailer demonstrates the approach’s practical value in identifying bottlenecks and suggesting improvement actions, showcasing the potential for improved inventory control in retail settings.